VGG-16 Technique to Reduce the Global Food Crises and Enhance the Crop Yields: Deep Learning Approaches

In the digital era, enhancing agriculture plays a critical role in addressing food crises by improving crop yield and ensuring food security. Early detection and management of crop diseases are paramount for agricultural sustainability, significantly affecting food security and farmer livelihoods. Farmers often struggle to identify crop diseases at early stages, leading to reduced yields and potential food shortages. This research aims to assist farmers in early disease identification, thereby increasing crop yield and preventing food crises. This study introduces a machine learning approach using Convolutional Neural Networks (CNNs), specifically the VGG-16 model, to accurately identify crop diseases. VGG-16’s architecture is particularly effective in recognizing patterns in images, making it a superior choice for diagnosing plant diseases compared to existing models. By leveraging VGG-16, the research demonstrates improved accuracy in detecting crop diseases at early stages, providing a valuable tool for farmers to manage crop health proactively. The findings of this research highlight the potential of advanced machine learning techniques to enhance agricultural practices and contribute to global food security.